Effects of a heat and moisture exchanger on respiratory function and symptoms post–cold air exercise
Bibliographic record
Abstract
Purpose Exercise at temperatures below −15°C induces drying and cooling of lung airways which causes exercise‐induced bronchoconstriction (EIB) and respiratory symptoms, especially in winter sport athletes. The objective of this study was to evaluate whether a heat and moisture exchanger (HME) worn during intense cold air exercise improves lung function and reduces respiratory symptoms in healthy winter sport athletes. Methods Seven active males and six active females (maximum oxygen uptake 61.9 ± 6.9 and 52.2 ± 5.3 mL/kg/min), all active or former winter sport athletes, completed running trials with and without HME in random order on 2 days in an environmental chamber (−20°C temperature, humidity 46.2%). Forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), forced expiratory flow at 25%‐75% (FEF25%‐75%), and FEF at 50% (FEF50%) were measured pre‐ and post‐exercise (3, 6, 10, 15, and 20 minutes). Respiratory symptoms were reported after exercise. Results Significant interaction effects were observed for FEV1 and FEF25%‐75%. Mean decrease of FVC (−5.9%, P ≤ .001) and FEV1 (−4.2%, P = .003) was largest 3 minutes post‐exercise without HME. There was an increase of FEV1, FEF25%‐75%, and FEF50% post‐exercise compared to pre‐exercise with HME. More respiratory symptoms overall were reported without HME (P = .046). Conclusion Intense cold air exercise likely causes transient acute bronchoconstriction and symptoms of cough in individuals participating in winter sports. However, this study finds that the application of an HME during intense cold air exercise improves lung function and reduces prevalence of EIB‐associated symptoms compared to unprotected intense cold air exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".